| Title |
Construction of Equipment Dataset and Model Design for SLA-based 3D Printing Process Optimization |
| Authors |
임태훈(Tae-Hoon Lim) ; 신화선(Hwa-Seon Shin) ; 하철우(Cheol-Woo Ha) ; 이혜인(Hye-In Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2024.73.4.738 |
| Keywords |
Machine Learning; Deep Learning; Addictive Manufacturing; SLA; 3DPrinter |
| Abstract |
This paper describes training data construction and analysis for developing a process-optimized artificial intelligence model to minimize errors occurring during the SLA-based additive manufacturing process. The photocurable resin molding method is a way in which UV lasers are irradiated into a tank containing liquid resin, solidified and stacked layer by layer, and like other additive manufacturing methods, there is an error in output deformation. However, due to the opaque resin, it is more difficult to check the error pattern than other method printer. In this study, to detect these error patterns, collecting data system in the actual process was established for sensor data, image data, and thermal image data and a study on data analysis was conducted |